Department of Technologies and Information Systems, Escuela Sup. Informática, Paseo de la Universidad 4, 13071, Ciudad Real, Spain.
Department of Technologies and Information Systems, Escuela Sup. Informática, Paseo de la Universidad 4, 13071, Ciudad Real, Spain.
Comput Methods Programs Biomed. 2020 Jul;191:105415. doi: 10.1016/j.cmpb.2020.105415. Epub 2020 Feb 24.
The amount of information available about millions of different subjects is growing every day. This has led to the birth of new search tools specialized in different domains, because classical information retrieval models have trouble dealing with the special characteristics of some of these domains. Evidence-based Medicine is a case of a complex domain where classical information retrieval models can help search engines retrieve documents by considering the presence or absence of terms, but these must be complemented with other specific strategies which allow retrieving and ranking documents including the best current evidence and methodological quality.
The goal is to present a ranking algorithm able to select the best documents for clinicians considering aspects related to the relevance and the quality of said documents.
In order to assess the effectiveness of this proposal, an experimental methodology has been followed by using Medline as a data set and the Cochrane Library as a gold standard.
Applying the evaluation methodology proposed, and after submitting 40 queries on the platform developed, the MAP (Mean Average Precision) obtained was 20.26%.
Successful results have been achieved with the experiments, improving on other studies, but under different and even more complex circumstances.
每天都有大量关于数百万个不同主题的信息可供获取。这导致了专门针对不同领域的新型搜索工具的诞生，因为经典的信息检索模型难以处理这些领域的一些特殊特征。循证医学就是一个复杂领域的案例，经典的信息检索模型可以通过考虑术语的存在与否来帮助搜索引擎检索文档，但这些模型必须辅以其他特定策略，这些策略允许检索和对包括最新最佳证据和方法学质量在内的文档进行排名。
旨在提出一种能够为临床医生选择最佳文档的排名算法，该算法考虑到了文档的相关性和质量等方面。
为了评估该建议的有效性，我们采用了实验方法，使用 Medline 作为数据集，Cochrane 图书馆作为黄金标准。
通过应用所提出的评估方法，并在开发的平台上提交了 40 个查询，获得的 MAP（平均平均精度）为 20.26%。
实验取得了成功的结果，与其他研究相比有所改进，但在不同且更加复杂的情况下仍有提升空间。
